To Graph or Not to Graph? life sciences context layer
Life Sciences

To Graph or Not to Graph?

Do life sciences teams need a graph for semantic AI , or is hybrid retrieval enough? A tier-based decision framework for enterprise context layers.

July 2026 · 14 min read Read article →
The Ideal Context Intelligence Engine
Architecture

The Ideal Context Intelligence Engine

How to design retrieval that works in regulated life sciences : ingestion, hybrid indexing, ranking, and governed context packaging.

July 2026 · 12 min read Read article →
Context and semantic models
Architecture

Context & Semantic Models: Architecting Without Wasting Tokens

The bottleneck isn't the model: it's what never reaches it. How to design a retrieval layer that sends only the signal your LLM needs , especially for unstructured, multi-modal corpora.

June 2026 · 12 min read Read article →
Benchmarking retrieval
Engineering

Benchmarking Retrieval: SOTA Models and What Actually Matters

DIY. A practical framework for evaluating embedding models and ranking pipelines on your own data , not someone else's leaderboard.

June 2026 · 11 min read Read article →

Designing a Good Retrieval Benchmark

Extended PDF guide : how to build evaluation sets, measure Recall@k and MRR on your corpus, and avoid leaderboard-driven mistakes when choosing embedding and ranking stacks.

To Graph or Not to Graph? Decision Matrix

Tier-based decision matrix for life sciences context architecture ; when hybrid retrieval is enough, and when governed mappings or a graph are required.

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